A recurrent neural model for proto-object based contour integration and figure-ground segregation

Abstract

Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects (“proto-objects”) based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation level biases the interpretation of the visual scene in agreement with principles from Gestalt psychology. Our model explains several sets of neurophysiological results (Zhou et al. Journal of Neuroscience, 20(17), 6594–6611 2000; Qiu et al. Nature Neuroscience, 10(11), 1492–1499 2007; Chen et al. Neuron, 82(3), 682–694 2014), and makes testable predictions about the influence of neuronal feedback and attentional selection on neural responses across different visual areas. Our model also provides a framework for understanding how object-based attention is able to select both objects and the features associated with them.

Notes

Acknowledgements

We would like to thank Justin Killebrew for his help in using the computational cluster in order to run the simulations. We would also like to thank Rüdiger von der Heydt for sharing his deep insights on vision with us.

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